Health Care Workers’ Experience With a Psychological Self-Monitoring App During the COVID-19 Pandemic: Mixed Methods Study
Bibliographic record
Abstract
Background Health care workers (HCWs) are at risk of experiencing psychological distress, particularly during the COVID-19 pandemic. Psychological self-monitoring apps may contribute to reducing symptoms of depression, anxiety, and trauma exposure by enhancing emotional self-awareness. This study focused on how a basic psychological self-monitoring app was experienced by HCWs during the COVID-19 pandemic in Quebec by exploring users’ experience and factors contributing to their adherence. Objective This study aimed to explore HCWs’ experiences with a psychological self-monitoring app, including if their satisfaction with the app, their perception of its contribution to self-awareness, and their experience of distress influenced their adherence to the app. Methods HCWs in Quebec were invited to respond weekly to questions about their well-being via a mobile app. A convergent mixed methods design was used. Sample data (N=424) were collected from the app, a postparticipation questionnaire was administered, and 30 semistructured interviews were conducted. Correlations and hierarchical multiple regression models were conducted to examine possible factors influencing participants’ adherence, and a thematic analysis was used to further explore their experience. Results Over a 12-week-period, mean adherence to the psychological self-monitoring app was 74.5% (SD 29.4%) and mean satisfaction was 80% (SD 20%). Most participants perceived that the app contributed moderately (165/418, 39.5%) or a lot (140/418, 33.5%) to enhancing their self-awareness. The significant regression model (F5,401=6.59; P<.001) suggested that around 7.6% of adherence variation could be explained by satisfaction (β=.16; t401=3.14; P=.002) and the app’s perceived contribution to self-awareness (β=.15; t401=2.88; P=.004). Biological sex (369/419, 88.1% female and 50/419, 11.9% male), age (mean 40.8, SD 9.9 y), and the experience of psychological distress at least once in 12 weeks (228/420, 54.3%) were not statistically significant predictors of adherence. Emergent themes from the 30 interviews highlighted participants’ experiences. Psychological self-monitoring was seen as an introspective practice, with reports of enhanced self-awareness and self-care practices. Interviewees generally considered the app as practical, but it did not suit everyone’s preferences. Potential app enhancements were provided by the participants. Conclusions A simple psychological self-monitoring app could be an interesting tool for HCWs who wish to improve their self-awareness and prevent psychological distress, particularly in health crises such as pandemics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".